We will be introducing the incredibly powerful and popular ggplot2 package
To whet everyones appetites, let’s take a look at just what is possible with the ggplot package in R. All of these visualizations were made in R, and their code is available online.
#Starting with the AQS data
ggplot(dat_ozone)
The first necessary item is the dataset you want to chart. In this instance, we will be starting with the AQS data that we used in the the open-air webinar.
Here, we passed the dataset(“dat_ozone”) as the first argument to ggplot.
Also, remember, since the data is the first argument, we can also pipe the dataset into ggplot
dat_ozone %>%
ggplot()
This provides the first requirement, but of course, doesn’t actually build a chart yet. It’s sort of like having data in a table in Excel - it’s necessary, but insufficient!
Next, you will define some aesthetic mappings to ggplot.
ggplot(dat_ozone, aes(x = DateTime, y = o3))
This looks like an empy plot, too, but what have we actually accomplished here?
The part of a chart that everyone is familir with is the geometry, or the “geom”, in ggplot2-speak. This defines the “type” of chart, in the sense of bar charts vs. line charts vs. scatterplots, etc.
We’ll start with a scatterplot, whose associated geometry, or geom, is “geom_point”
ggplot(dat_ozone, aes(x = DateTime, y = o3)) +
geom_point()## Warning: Removed 107 rows containing missing values (geom_point).
Let’s talk through this output. First to note - we recieved a warning symbol - the message being, “Removed 107 rows containing missing values (geom_point)”. Remember in the “working with data” webinar, that R takes missing data VERY seriously.
The sort of “catch-all” way to remove NA values from your commands is, “na.rm = TRUE”. So, if we pass that to the geom call, we will get the same result, but without the warning.
ggplot(dat_ozone, aes(x = DateTime, y = o3)) +
geom_point(na.rm = TRUE)
So, we have a scatterplot geom that has been mapped onto the exact chart space we had before we called a geomgetry. Remember, our aes() call supplied the scale, and the geom just maps the geometric object onto the datapoints. But, this scatterplot isn’t so great. The first issue is that each day has 24 points, and we’re mapping an entire year. That’s just too much data to make sense of in this way.
Let’s take a look at how we might subset this data, and just look at the summer ozone season.
dat_ozone %>%
filter(DateTime >= as.Date('2017-05-01') & DateTime <= as.Date('2017-10-01')) %>%
ggplot(aes(x = DateTime, y = o3)) +
geom_point(na.rm = TRUE)
We are using more of the skills we learned in the “working with data” webinar! We piped the dataset to the filter “verb”, and selected only the DateTimes after May 1 AND before Oct 1.
dat_ozone %>%
filter(DateTime >= as.Date('2017-05-01') & DateTime <= as.Date('2017-10-01')) %>%
ggplot(aes(x = DateTime, y = o3)) +
geom_point(na.rm = TRUE) +
geom_smooth(method = lm)## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 43 rows containing non-finite values (stat_smooth).
While a linear model line plotted on this dataset isn’t actually that informative, it shows that you can build more complex R charts by adding geoms to your plot! In this case, there are two geoms - point and smooth.
I wanted to talk a little more about this “aesthetic” mapping, and how to do in in ggplot.
In all the above code, we defined the aes() call in the intial ggplot call. Like this:
ggplot(dat_ozone, aes(x = DateTime, y = o3)) +
geom_point()## Warning: Removed 107 rows containing missing values (geom_point).
However, looks what happens when we move the aes() call to the geom, like this:
dat_ozone %>%
ggplot() +
geom_point(aes(x = DateTime, y = o3), na.rm = TRUE)
Some examples:
dat_ozone %>%
filter(DateTime >= as.Date('2017-05-01') & DateTime <= as.Date('2017-10-01')) %>%
ggplot(aes(x = DateTime, y = o3)) +
geom_point(na.rm = TRUE) +
geom_smooth(method = lm)## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 43 rows containing non-finite values (stat_smooth).
#same as:
dat_ozone %>%
filter(DateTime >= as.Date('2017-05-01') & DateTime <= as.Date('2017-10-01')) %>%
ggplot() +
geom_point(aes(x = DateTime, y = o3), na.rm = TRUE) +
geom_smooth(aes(x = DateTime, y = o3), method = lm)## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 43 rows containing non-finite values (stat_smooth).
dat_ozone %>%
filter(DateTime >= as.Date('2017-05-01') & DateTime <= as.Date('2017-10-01')) %>%
ggplot(aes(x = DateTime, y = o3)) +
geom_point(na.rm = TRUE) +
geom_col(data = dat_ozone_long_daily_summer, aes(x = as.POSIXct(Date), y = `Daily Avg`)) #we are using a different dataset and aes mapping for this geom## Warning: Removed 27 rows containing missing values (position_stack).
dat_ozone %>%
filter(DateTime >= as.Date('2017-05-01') & DateTime <= as.Date('2017-10-01')) %>%
ggplot(aes(x = DateTime, y = o3)) +
geom_point(na.rm = TRUE) +
geom_point(data = dat_ozone_long_daily_summer, aes(x = as.POSIXct(Date), y = `Daily Avg`), color = "red", size = 3, shape =17 )## Warning: Removed 27 rows containing missing values (geom_point).
Forget, for a moment, that this is an ugly chart, and focus, rather on what we accomplished. We were able to map two different gemoetries onto the same chart!
There is another layet that we haven’t talked about yet. The Statistic layer. This is often overlooked, as the geom you use tends to do this correctly behind the scenes. However, sometimes your chart won’t work correctly, and it’s because the statistical transformation that ggplot is trying to do can’t be done the way you told it to.
For example, a Bar chart. The numbers the bar chart is plotting are not numbers reported in the dataset. There is an underlying calculation happening - it counts the rows and plots THAT number.
dat_ozone_long %>%
filter(DateTime >= as.Date('2017-05-01') & DateTime <= as.Date('2017-10-01')) %>%
ggplot(aes(x = Measure)) +
geom_bar()
For instance, what we have shown with this barchart is that there are ~3700 measurements for each “Measure” in our dataset. Also notice that we only fed the geom_bar an x aesthetic - that’s because you can’t map a y aesthetic. For instance:
Another stat that is being done behind the scenes is in a histogram.
dat_ozone_long %>%
filter(DateTime >= as.Date('2017-05-01') & DateTime <= as.Date('2017-10-01'), Measure == "o3") %>%
ggplot(aes(x = Values)) +
geom_histogram() +
scale_y_continuous(limits = c(0, 400), expand = c(0,0)) + #commands to set the scale manually. This also removed the whitespace below "0" and above the x axis. Try removing it and see for yourself.
labs(title = "Ozone values histogram")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 43 rows containing non-finite values (stat_bin).
#?geom_histogram - You can see in the documentation that the stat being done is, "bin". In this example, we are weven given a a response from the system, saying, “stat_bin() using bins = 30. Pick better value with binwidth”. This is because we didn’t povide a binwidth for the histogram, so the stat used the default value of 30 bins.
We won’t dive more into statistics in charts in this webinar. However, You should be aware that it is an important layer of the grammar of graphics, and you will become more familiar with them as you chart more.
Another layer that can be added to a ggplot visualization is a facet.
dat_ozone_long %>%
filter(Measure == c("o3", "nox", "so2")) %>%
ggplot() +
geom_point(aes(x = DateTime, y = Values, color = Measure)) +
facet_wrap(~ Measure) + #this does the faceting!
theme_bw() +#this sets the theme to black and white, making a cleaner plot
guides(colour = guide_legend(override.aes = list(size=5))) #this changes the size of the points in the legend!## Warning: Removed 290 rows containing missing values (geom_point).
What is this beautiful chart!? This is a three-panel faceted chart of our dat_ozone_long dataset. We have faceted on the Measure, or pollutant, variable. But why are O3 and SO2 at zero? It’s because the three facets share the same Y axis. When you facet a chart, you are usually doing it to compare across facets. This means that you should share a Y axis.
In this case, it doesn’t make sense, because the pollutants have different concentrations. We can “free” the Y axis, so there is a seperate Y axis for each facet. BUT! We should make sure to draw the reader’s attention to the Y - that way, no one can accuse us of deceptive chart practices!
dat_ozone_long %>%
filter(Measure == c("o3", "nox", "so2")) %>%
ggplot() +
geom_point(aes(x = DateTime, y = Values, color = Measure)) +
facet_wrap(~ Measure, scales = "free_y") + #this does the faceting! and "free_y" allows a different Y axis for each facet.
theme_bw() +#this sets the theme to black and white, making a cleaner plot
guides(colour = guide_legend(override.aes = list(size=5))) + #this changes the size of the points in the legend!
labs(title = "Daily pollution concentrations in 2017",
subtitle = "Each chart has seperate y-axis of concentration")## Warning: Removed 290 rows containing missing values (geom_point).
Let’s do another example of a faceted chart. This time, we used mutate to create some new variables, then faceted on those variables (month and day of month).
Calendar_plot <- dat_ozone_long %>%
mutate("Month" = month(DateTime, label = T),
"Day of Month" = mday(DateTime)) %>% #these two lines create a "month" to facet on, and a "day of month" for the x axis
filter(Measure == "o3") %>% # I just want to plot Ozone
ggplot() +
geom_point(aes(x = `Day of Month`, y = Values), color = "Orange") +
facet_wrap(~ Month) +
theme_bw() +
labs(title = "Monthly Ozone values in 2017",
y = "Ozone ppb")
Calendar_plot## Warning: Removed 107 rows containing missing values (geom_point).
I use the coord_flip a lot, to make charts like this:
It’s really helpful to flip your bar chart if you have long titles for your factors. And the code for this plot:
# Data Prep
data("mtcars") # load data
mtcars$`car name` <- rownames(mtcars) # create new column for car names
mtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2) # compute normalized mpg
mtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, "below", "above") # above / below avg flag
mtcars <- mtcars[order(mtcars$mpg_z), ] # sort
mtcars$`car name` <- factor(mtcars$`car name`, levels = mtcars$`car name`) # convert to factor to retain sorted order in plot.
# Diverging Barcharts
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +
geom_bar(stat='identity', aes(fill=mpg_type), width=.5) +
scale_fill_manual(name="Mileage",
labels = c("Above Average", "Below Average"),
values = c("above"="#00ba38", "below"="#f8766d")) +
labs(subtitle="Normalised mileage from 'mtcars'",
title= "Diverging Bars") +
coord_flip() #try it without the coord_flip! It's awful...
coord_polar:
rect <- data.frame(x = 50, y = 50)
line <- data.frame(x = c(1, 200), y = c(100, 1))
base <- ggplot(mapping = aes(x, y)) +
geom_tile(data = rect, aes(width = 50, height = 50)) +
geom_line(data = line) +
xlab(NULL) + ylab(NULL)
base
base + coord_polar("x")
base + coord_polar("y")
Chart labels, that is! You should always label your charts, to make them easy to udnerstand for your audience. ggplot has numerous ways to add labels, but the easiest is probably with the “labs” call
dat_ozone %>%
filter(DateTime >= as.Date('2017-05-01') & DateTime <= as.Date('2017-10-01')) %>%
ggplot(aes(x = DateTime, y = o3)) +
geom_point(na.rm = TRUE) +
labs(title = "Hourly Ozone Concentrations",
subtitle = "Time series chart for the summer of 2017",
x = "Date",
y = "Ozone (ppb)")
There are other packages that may help to make more custom annotations. One example - What if I wanted to add “DRAFT” over the graphic?
library(cowplot)## Warning: package 'cowplot' was built under R version 3.6.3##
## ********************************************************## Note: As of version 1.0.0, cowplot does not change the## default ggplot2 theme anymore. To recover the previous## behavior, execute:
## theme_set(theme_cowplot())## ********************************************************##
## Attaching package: 'cowplot'## The following object is masked from 'package:lubridate':
##
## stampggdraw(Calendar_plot) +
draw_label("Draft", color = "#C0A0A0", size = 200, angle = 45)## Warning: Removed 107 rows containing missing values (geom_point).
Something I wanted to show everyone. It was about 1.5 years into my R education when I really started using functions, so it isn’t something I expect anyone to be able to do now. BUT! This function is a really cool way to loop over all your variables, and make plots for each variable. I use it in data exploration a lot.
#Let's write a function that lets us iterate over the pollutants, and make a new chart for each pollutant
Pollutants <- (NEI_totals_long %>%
distinct(NEI_totals_long$Pollutant))
Pollutants <- Pollutants[[1]]
plot_pols = function(df, pol){
for(pol in Pollutants){
p = ggplot(NEI_totals_long %>%
filter(.data$Pollutant == pol)) +
geom_line(aes(x = factor(Year), y = `Tons per Year`, color = Sector, group = Sector), size = 1.2) +
geom_point(aes(x = factor(Year), y = `Tons per Year`, color = Sector), size = 2) +
scale_fill_brewer(palette = "Set2") +
labs(title = paste0(pol," Annual Tons, by Sector")) +
scale_x_discrete(expand = expansion(mult = c(0.01, 0.05)))+
theme_bw()
print(p)
}
}
plot_pols(NEI_totals_long, Pollutants)
